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  string(118) "Predictors of Clinical Response in Non-radiographic Axial Spondyloarthritis; a post hoc analysis of the GO-AHEAD trial"
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  string(694) "Spondyloarthritis is a family of diseases that includes inflammation of the spine and commonly results in restricted motion and disability.  Ankylosing spondylitis, the prototypic form of the disease, is characterized by bony changes on X-ray.  Earlier or milder disease, without X ray changes is called non-radiographic axial spondyloarthritis (nr-axSpA).  The goal of this work is to improve clinical outcomes and quality of life of people with nr-axSpA, through earlier disease recognition and enhanced access to effective therapies. We will  perform statistical analyses to establish subject-level characteristics among persons with nr-axSpA who were in a trial of Golimumab  (NCT01453725)."
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  ["property_scientific_abstract"]=>
  string(1549) "Background: Spondyloarthritis is a family of diseases that includes inflammation of the spine and commonly results in restricted motion and disability.  Effective treatment is commonly delayed, and this contributes to worse outcomes.
Objective: We propose studies to establish predictors of clinical improvement (Assessment of spondyloarthritis international society 40% improvement [ASAS 40]) through a post-hoc analysis of data collected in the ?GO-AHEAD? trial of golimumab in non-radiographic axial spondyloarthritis.
Study Design: We will use previously collected subject-level characteristics to construct regression models to predict ASAS40 improvement at week 16 among subjects treated with golimumab or placebo in the GO-AHEAD Trial. Outside of the data-sharing platform, we will request raw MRI images (baseline pelvis) to apply a novel MRI technology predicting ASAS40 improvement. We will then compare models incorporating both baseline clinical predictors and the prediction of the MRI technology using standard model fit statistics.
Participants: All subjects treated with either golimumab or placebo in the GO-AHEAD trial
Main Outcome Measures: Assessment of spondyloarthritis international society 40% improvement (ASAS 40) at week 16
Statistical Analysis: We will construct regression models as above. We will compare the clinical model, MRI-only and the integrated models by assessing the accuracy, sensitivity, specificity and by plotting receiver operating characteristics (ROC) for each model." ["project_brief_bg"]=> string(1433) "Spondyloarthritis is a family of diseases that includes inflammation of the spine and commonly results in restricted motion and disability. Ankylosing spondylitis, the prototypic form of the disease, is characterized by bony changes on X-ray. Earlier or milder disease, however, may have changes visible only on MRI, and has been called non-radiographic axial spondyloarthritis (nr-axSpA). The classification of nr-axSpA remains controversial in terms of the magnetic resonance Imaging (MRI) lesions that define the presence of sacroillitis. While early studies reported that bone marrow edema and erosions were specific for nr-axSpA, subsequent work found such lesions to be present in those with mechanical causes for back pain, such as healthy athletes. Therefore, the goal of this work is to construct multivariable models predicting clinical improvement, and compare these models with novel MRI-based models clinical improvement.
The goal of this work is to improve clinical outcomes and quality of life of people with nr-axSpA, through earlier disease recognition and enhanced access to effective therapies. To this end, this application seeks to establish subject-level characteristics among nr-axSpA trial subjects who responded to Tumor necrosis factor inhibitor (TNFi) treatment or placebo. This work will leverage a unique collaboration of experts in spondyloarthritis, epidemiology and musculoskeletal imaging." ["project_specific_aims"]=> string(616) "Aim 1. To assess the ability of subject- level characteristic to predict clinical improvement among nr-axSpA subjects who were treated with golimumab or placebo in the MK-8259-006-02 (GO-AHEAD) trial.
Aim 2. To develop novel MRI image assessment techniques for use in sacroiliac joint (pelvic) MRIs among subjects with nr-axSpA who were treated with golimumab or placebo in GO-AHEAD. Access to this data will be negotiated outside of this platform, independent of this proposal.
Aim 3. To compare the algorithms developed in Aims 1 and 2 in predicting clinical improvement, using model fit statistics." ["project_study_design"]=> string(0) "" ["project_study_design_exp"]=> string(0) "" ["project_purposes"]=> array(1) { [0]=> array(2) { ["value"]=> string(50) "Research on clinical prediction or risk prediction" ["label"]=> string(50) "Research on clinical prediction or risk prediction" } } ["project_purposes_exp"]=> string(0) "" ["project_software_used"]=> array(2) { ["value"]=> string(1) "R" ["label"]=> string(1) "R" } ["project_software_used_exp"]=> string(0) "" ["project_research_methods"]=> string(95) "All subjects from the study who were treated with either golimumab or placebo will be included." ["project_main_outcome_measure"]=> string(580) "Primary outcome: Assessment of spondyloarthritis international society 40% improvement (ASAS 40) at week 16.
Secondary outcomes (for sensitivity analyses) will include: Assessment of spondyloarthritis international society 20% improvement (ASAS 20) achievement, Bath ankylosing spondylitis disease activity index (BASDAI) 50% improvement (ie BASDAI 50), Assessment of spondyloarthritis international society (ASAS) partial remission, and change in Spondylitis Research Consortium of Canada (SPARCC) score for MRI pelvis, which were all secondary trial outcomes in GO-AHEAD." ["project_main_predictor_indep"]=> string(61) "Treatment arm (golimumab versus placebo), intention to treat." ["project_other_variables_interest"]=> string(1180) "Age at enrollment, sex, disease duration (years), family history of spondyloarthritis, smoking status (current/prior/never), prior use of conventional synthetic disease modifying anti-rheumatic drugs (csDMARDs), baseline High sensitivity CRP (hsCRP) value, baseline Erythrocyte sedimentation rate (ESR) value, baseline Spondylitis Research Consortium of Canada (SPARCC) score for MRI pelvic and spine, baseline Health assessment questionnaire- disability index (HAQ-DI), baseline Ankylosing spondylitis Disease Activity Score (ASDAS), baseline BASDAI, fulfillment of ASAS axial spondyloarthritis classification criteria at baseline (ie- clinical versus imaging arm), determination of MRI sacroiliitis according to central MRI reader (present versus absent).
Outside of this platform, we will request raw images from baseline pelvic/sacroiliac MRIs. A novel technology will be applied to the MRI images to predict clinical improvement. In order to do this, we will require baseline pelvic MRI images (~150 images per subject), from T1 and T2 fat-suppressed semi-coronal images of the sacroiliac joints.
**THIS REQUEST IS NOT FOR MRI IMAGES THROUGH THE YODA PLATFORM**" ["project_stat_analysis_plan"]=> string(3618) "Outside of this platform, we will request raw images from baseline pelvic/sacroiliac MRIs. A novel technology will be applied to the MRI images to predict clinical improvement. In order to do this, we will require baseline pelvic MRI images (~150 images per subject), from T1 and T2 fat-suppressed semi-coronal images of the sacroiliac joints.
Data from the AbbVie ?ABILITY-1? study of adalimumab in nr-axSpA has been requested to be provided in another data sharing platform, Vivli. We have requested data from the UCB trial of certolizumab pegol in nr-axSpA ?C-axSpAnd, which will be negotiated and provided outside of any data sharing platform. We will additionally request raw MRI images from the 2 other trials to be pooled with the MRIs from the GO-AHEAD study and analyzed using the novel technology outside of any data sharing platform subject to contractual negotiations separately from the Vivli data. The novel MRI technology will generate a numeric value predicting ASAS40 improvement, which we can then compare to predictive models using clinical variables as outlined below.
Clinical variables at baseline (as described above) will be used to construct regression models to predict ASAS40 improvement. We will construct models using a broad group of clinical variables from the trial datasets, with selection of variables using a random forest method. The clinical model with the best model fit statistics will be compared with MRI prediction only, and an integrated model predicting ASAS40 improvement. The integrated model will be constructed by including the MRI technology output (prediction of ASAS40 improvement) in the optimal clinical model to predict ASAS40 improvement.
While it is our hope that the data sharers for the 3 trials under study agree to share data on the same data sharing platform (Janssen has moved data to Vivli in the past), we recognize this may not be possible for this study. If data from all 3 studies is hosted on the same platform, we will perform a pooled analysis of the clinical data, incorporating trial as a variable in the regression model.
If it is not possible to pool data from all 3 trials on the same platform, then we will use standard meta-analytic techniques to combine models derived from the 3 trials. If we perform meta-analysis, we will export only summary level results (eg- odds ratios) for the variables incorporated in the model, and a weighting variable to account for differences in study size. This meta-analysis of summary-level effect estimates would be performed on a local computer at Boston University that is password protected and located in a locked office. We will not ever attempt to export individual-level data.
We will compare the clinical model, MRI-only and the integrated models by assessing the accuracy, sensitivity, specificity and by plotting receiver operating characteristics (ROC) for each model. Confidence intervals for these model performance measures will be obtained by bootstrap sampling using 100% of the test data with replacement (100 iterations). Area under the curve (AUC) will be calculated for each bootstrap iteration, using student?s t test with 99 degrees of freedom. After generating models that include all subjects, we will additionally construct and test sex-specific models.
Note that further negotiations regarding sharing and use of data regarding the MRI images from the trial will take place between the investigator and the data sharing organization, outside of this platform. **THIS REQUEST IS NOT FOR MRI IMAGES THROUGH THE YODA PLATFORM**" ["project_timeline"]=> string(333) "The MRI technology component would be performed from Jan 2021 to July 2024.
The start date for this project is expected to be August 2023
Completion date for this project is expected to be July 2024
Date results reported back to YODA: September 2024
Date manuscript submitted for publication: December 2024" ["project_dissemination_plan"]=> string(239) "American College of Rheumatology annual meetings, 2022-2025
Manuscript submission to leading North American and/or European Rheumatology Journals: Arthritis Care & Research, Arthritis & Rheumatology, Annals of the Rheumatic Diseases" ["project_bibliography"]=> string(2402) "

Rudwaleit M, et al., The development of Assessment of SpondyloArthritis international Society classification criteria for axial spondyloarthritis (part II): validation and final selection. Ann Rheum Dis, 2009. 68: p. 777-83.
Maksymowych WP, L.R., stergaard M, Pedersen SJ, Machado PM, Weber U, Bennett AN, Braun J, Burgos-Vargas R, de Hooge M, Deodhar AA, Eshed I, Jurik AG, Hermann KA, Landew RB, Marzo-Ortega H, Navarro-Compn V, Poddubnyy D, Reijnierse M, Rudwaleit M, Sieper J, Van den Bosch FE, van der Heijde D, van der Horst-Bruinsma IE, Wichuk S, Baraliakos X, MRI lesions in the sacroiliac joints of patients with spondyloarthritis: an update of definitions and validation by the ASAS MRI working group. Ann Rheum Dis, 2019. 78(11): p. 1550-1558
Deodhar A, G.L., Kay J, Maksymowych WP, Haroon N, Landew R, Rudwaleit M, Hall S, Bauer L, Hoepken B, de Peyrecave N, Kilgallen B, van der Heijde D., A Fifty-Two-Week, Randomized, Placebo-Controlled Trial of Certolizumab Pegol in Nonradiographic Axial Spondyloarthritis. Arthritis Rheumatol, 2016. 71(7): p. 1101-11.
Sieper J, v.d.H.D., Dougados M, Mease PJ, Maksymowych WP, Brown MA, Arora V, Pangan AL. . Ann Rheum Dis. 2013 Jun;72(6):815-22, Efficacy and safety of adalimumab in patients with non-radiographic axial spondyloarthritis: results of a randomised placebo-controlled trial (ABILITY-1). Ann Rheum Dis, 2013. 72(6): p. 815-22.
Sieper J, v.d.H.D., Dougados M, Maksymowych WP, Scott BB, Boice JA, Berd Y, Bergman G, Curtis S, Tzontcheva A, Huyck S, Weng HH. , A randomized, double-blind, placebo-controlled, sixteen-week study of subcutaneous golimumab in patients with active nonradiographic axial spondyloarthritis. Arthritis Rheumatol, 2015. 67(10): p. 2702-12.
Dietrich S, F.A., Troll M, Khn T, Rathmann W, Peters A, Sookthai D, von Bergen M, Kaaks R, Adamski J, Prehn C, Boeing H, Schulze MB, Illig T, Pischon T, Knppel S, Wang-Sattler R, Drogan D. , Random survival forest in practice: a method for modelling complex metabolomics data in time to event analysis. Int. J. Epidemiol., 2016. 45: p. 1406-20.
Benjamini Y, H.Y., Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B Met, 1995. 57: p. 289-300.
Brock G, D.S., Pihur V, Datta S., An R package for cluster validation. Journal of Statistical Software, 2008. 25: p. 1-22

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2019-4132

Research Proposal

Project Title: Predictors of Clinical Response in Non-radiographic Axial Spondyloarthritis; a post hoc analysis of the GO-AHEAD trial

Scientific Abstract: Background: Spondyloarthritis is a family of diseases that includes inflammation of the spine and commonly results in restricted motion and disability. Effective treatment is commonly delayed, and this contributes to worse outcomes.
Objective: We propose studies to establish predictors of clinical improvement (Assessment of spondyloarthritis international society 40% improvement [ASAS 40]) through a post-hoc analysis of data collected in the ?GO-AHEAD? trial of golimumab in non-radiographic axial spondyloarthritis.
Study Design: We will use previously collected subject-level characteristics to construct regression models to predict ASAS40 improvement at week 16 among subjects treated with golimumab or placebo in the GO-AHEAD Trial. Outside of the data-sharing platform, we will request raw MRI images (baseline pelvis) to apply a novel MRI technology predicting ASAS40 improvement. We will then compare models incorporating both baseline clinical predictors and the prediction of the MRI technology using standard model fit statistics.
Participants: All subjects treated with either golimumab or placebo in the GO-AHEAD trial
Main Outcome Measures: Assessment of spondyloarthritis international society 40% improvement (ASAS 40) at week 16
Statistical Analysis: We will construct regression models as above. We will compare the clinical model, MRI-only and the integrated models by assessing the accuracy, sensitivity, specificity and by plotting receiver operating characteristics (ROC) for each model.

Brief Project Background and Statement of Project Significance: Spondyloarthritis is a family of diseases that includes inflammation of the spine and commonly results in restricted motion and disability. Ankylosing spondylitis, the prototypic form of the disease, is characterized by bony changes on X-ray. Earlier or milder disease, however, may have changes visible only on MRI, and has been called non-radiographic axial spondyloarthritis (nr-axSpA). The classification of nr-axSpA remains controversial in terms of the magnetic resonance Imaging (MRI) lesions that define the presence of sacroillitis. While early studies reported that bone marrow edema and erosions were specific for nr-axSpA, subsequent work found such lesions to be present in those with mechanical causes for back pain, such as healthy athletes. Therefore, the goal of this work is to construct multivariable models predicting clinical improvement, and compare these models with novel MRI-based models clinical improvement.
The goal of this work is to improve clinical outcomes and quality of life of people with nr-axSpA, through earlier disease recognition and enhanced access to effective therapies. To this end, this application seeks to establish subject-level characteristics among nr-axSpA trial subjects who responded to Tumor necrosis factor inhibitor (TNFi) treatment or placebo. This work will leverage a unique collaboration of experts in spondyloarthritis, epidemiology and musculoskeletal imaging.

Specific Aims of the Project: Aim 1. To assess the ability of subject- level characteristic to predict clinical improvement among nr-axSpA subjects who were treated with golimumab or placebo in the MK-8259-006-02 (GO-AHEAD) trial.
Aim 2. To develop novel MRI image assessment techniques for use in sacroiliac joint (pelvic) MRIs among subjects with nr-axSpA who were treated with golimumab or placebo in GO-AHEAD. Access to this data will be negotiated outside of this platform, independent of this proposal.
Aim 3. To compare the algorithms developed in Aims 1 and 2 in predicting clinical improvement, using model fit statistics.

Study Design:

What is the purpose of the analysis being proposed? Please select all that apply.: Research on clinical prediction or risk prediction

Software Used: R

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: All subjects from the study who were treated with either golimumab or placebo will be included.

Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study: Primary outcome: Assessment of spondyloarthritis international society 40% improvement (ASAS 40) at week 16.
Secondary outcomes (for sensitivity analyses) will include: Assessment of spondyloarthritis international society 20% improvement (ASAS 20) achievement, Bath ankylosing spondylitis disease activity index (BASDAI) 50% improvement (ie BASDAI 50), Assessment of spondyloarthritis international society (ASAS) partial remission, and change in Spondylitis Research Consortium of Canada (SPARCC) score for MRI pelvis, which were all secondary trial outcomes in GO-AHEAD.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: Treatment arm (golimumab versus placebo), intention to treat.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: Age at enrollment, sex, disease duration (years), family history of spondyloarthritis, smoking status (current/prior/never), prior use of conventional synthetic disease modifying anti-rheumatic drugs (csDMARDs), baseline High sensitivity CRP (hsCRP) value, baseline Erythrocyte sedimentation rate (ESR) value, baseline Spondylitis Research Consortium of Canada (SPARCC) score for MRI pelvic and spine, baseline Health assessment questionnaire- disability index (HAQ-DI), baseline Ankylosing spondylitis Disease Activity Score (ASDAS), baseline BASDAI, fulfillment of ASAS axial spondyloarthritis classification criteria at baseline (ie- clinical versus imaging arm), determination of MRI sacroiliitis according to central MRI reader (present versus absent).
Outside of this platform, we will request raw images from baseline pelvic/sacroiliac MRIs. A novel technology will be applied to the MRI images to predict clinical improvement. In order to do this, we will require baseline pelvic MRI images (~150 images per subject), from T1 and T2 fat-suppressed semi-coronal images of the sacroiliac joints.
**THIS REQUEST IS NOT FOR MRI IMAGES THROUGH THE YODA PLATFORM**

Statistical Analysis Plan: Outside of this platform, we will request raw images from baseline pelvic/sacroiliac MRIs. A novel technology will be applied to the MRI images to predict clinical improvement. In order to do this, we will require baseline pelvic MRI images (~150 images per subject), from T1 and T2 fat-suppressed semi-coronal images of the sacroiliac joints.
Data from the AbbVie ?ABILITY-1? study of adalimumab in nr-axSpA has been requested to be provided in another data sharing platform, Vivli. We have requested data from the UCB trial of certolizumab pegol in nr-axSpA ?C-axSpAnd, which will be negotiated and provided outside of any data sharing platform. We will additionally request raw MRI images from the 2 other trials to be pooled with the MRIs from the GO-AHEAD study and analyzed using the novel technology outside of any data sharing platform subject to contractual negotiations separately from the Vivli data. The novel MRI technology will generate a numeric value predicting ASAS40 improvement, which we can then compare to predictive models using clinical variables as outlined below.
Clinical variables at baseline (as described above) will be used to construct regression models to predict ASAS40 improvement. We will construct models using a broad group of clinical variables from the trial datasets, with selection of variables using a random forest method. The clinical model with the best model fit statistics will be compared with MRI prediction only, and an integrated model predicting ASAS40 improvement. The integrated model will be constructed by including the MRI technology output (prediction of ASAS40 improvement) in the optimal clinical model to predict ASAS40 improvement.
While it is our hope that the data sharers for the 3 trials under study agree to share data on the same data sharing platform (Janssen has moved data to Vivli in the past), we recognize this may not be possible for this study. If data from all 3 studies is hosted on the same platform, we will perform a pooled analysis of the clinical data, incorporating trial as a variable in the regression model.
If it is not possible to pool data from all 3 trials on the same platform, then we will use standard meta-analytic techniques to combine models derived from the 3 trials. If we perform meta-analysis, we will export only summary level results (eg- odds ratios) for the variables incorporated in the model, and a weighting variable to account for differences in study size. This meta-analysis of summary-level effect estimates would be performed on a local computer at Boston University that is password protected and located in a locked office. We will not ever attempt to export individual-level data.
We will compare the clinical model, MRI-only and the integrated models by assessing the accuracy, sensitivity, specificity and by plotting receiver operating characteristics (ROC) for each model. Confidence intervals for these model performance measures will be obtained by bootstrap sampling using 100% of the test data with replacement (100 iterations). Area under the curve (AUC) will be calculated for each bootstrap iteration, using student?s t test with 99 degrees of freedom. After generating models that include all subjects, we will additionally construct and test sex-specific models.
Note that further negotiations regarding sharing and use of data regarding the MRI images from the trial will take place between the investigator and the data sharing organization, outside of this platform. **THIS REQUEST IS NOT FOR MRI IMAGES THROUGH THE YODA PLATFORM**

Narrative Summary: Spondyloarthritis is a family of diseases that includes inflammation of the spine and commonly results in restricted motion and disability. Ankylosing spondylitis, the prototypic form of the disease, is characterized by bony changes on X-ray. Earlier or milder disease, without X ray changes is called non-radiographic axial spondyloarthritis (nr-axSpA). The goal of this work is to improve clinical outcomes and quality of life of people with nr-axSpA, through earlier disease recognition and enhanced access to effective therapies. We will perform statistical analyses to establish subject-level characteristics among persons with nr-axSpA who were in a trial of Golimumab (NCT01453725).

Project Timeline: The MRI technology component would be performed from Jan 2021 to July 2024.
The start date for this project is expected to be August 2023
Completion date for this project is expected to be July 2024
Date results reported back to YODA: September 2024
Date manuscript submitted for publication: December 2024

Dissemination Plan: American College of Rheumatology annual meetings, 2022-2025
Manuscript submission to leading North American and/or European Rheumatology Journals: Arthritis Care & Research, Arthritis & Rheumatology, Annals of the Rheumatic Diseases

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